Explaining a Neural Attention Model for Aspect-Based Sentiment Classification Using Diagnostic Classification

TL;DR

This paper introduces a diagnostic classification approach to interpret the internal workings of a neural attention model for aspect-based sentiment classification, revealing how different layers encode linguistic and sentiment information.

Contribution

It applies diagnostic classification to analyze and explain the internal representations of a neural attention model in ABSC, providing insights into layer-specific encoding.

Findings

Lower layers encode part of speech and sentiment

Higher layers encode aspect relations and aspect-related sentiment

Model interpretability is improved through diagnostic analysis

Abstract

Many high performance machine learning models for Aspect-Based Sentiment Classification (ABSC) produce black box models, and therefore barely explain how they classify a certain sentiment value towards an aspect. In this paper, we propose explanation models, that inspect the internal dynamics of a state-of-the-art neural attention model, the LCR-Rot-hop, by using a technique called Diagnostic Classification. Our diagnostic classifier is a simple neural network, which evaluates whether the internal layers of the LCR-Rot-hop model encode useful word information for classification, i.e., the part of speech, the sentiment value, the presence of aspect relation, and the aspect-related sentiment value of words. We conclude that the lower layers in the LCR-Rot-hop model encode the part of speech and the sentiment value, whereas the higher layers represent the presence of a relation with the aspect and the aspect-related sentiment value of words.